Refa: refine search app (#9536)

### What problem does this PR solve?

Refine search app.

### Type of change

- [x] Refactoring

api/apps/sdk/session.py

@@ -902,10 +902,16 @@ def ask_about_embedded():
     req = request.json
     uid = objs[0].tenant_id
 
+    search_id = req.get("search_id", "")
+    search_config = {}
+    if search_id:
+        if search_app := SearchService.get_detail(search_id):
+            search_config = search_app.get("search_config", {})
+
     def stream():
         nonlocal req, uid
         try:
-            for ans in ask(req["question"], req["kb_ids"], uid):
+            for ans in ask(req["question"], req["kb_ids"], uid, search_config):
                 yield "data:" + json.dumps({"code": 0, "message": "", "data": ans}, ensure_ascii=False) + "\n\n"
         except Exception as e:
             yield "data:" + json.dumps({"code": 500, "message": str(e), "data": {"answer": "**ERROR**: " + str(e), "reference": []}}, ensure_ascii=False) + "\n\n"
@@ -1021,8 +1027,19 @@ def related_questions_embedded():
     tenant_id = objs[0].tenant_id
     if not tenant_id:
         return get_error_data_result(message="permission denined.")
+
+    search_id = req.get("search_id", "")
+    search_config = {}
+    if search_id:
+        if search_app := SearchService.get_detail(search_id):
+            search_config = search_app.get("search_config", {})
+
     question = req["question"]
-    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
+
+    chat_id = search_config.get("chat_id", "")
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_id)
+
+    gen_conf = search_config.get("llm_setting", {"temperature": 0.9})
     prompt = load_prompt("related_question")
     ans = chat_mdl.chat(
         prompt,
@@ -1035,7 +1052,7 @@ Related search terms:
     """,
             }
         ],
-        {"temperature": 0.9},
+        gen_conf,
     )
     return get_json_result(data=[re.sub(r"^[0-9]\. ", "", a) for a in ans.split("\n") if re.match(r"^[0-9]\. ", a)])
 
@@ -1083,15 +1100,62 @@ def mindmap():
 
     tenant_id = objs[0].tenant_id
     req = request.json
+
+    search_id = req.get("search_id", "")
+    search_config = {}
+    if search_id:
+        if search_app := SearchService.get_detail(search_id):
+            search_config = search_app.get("search_config", {})
+
     kb_ids = req["kb_ids"]
+    if search_config.get("kb_ids", []):
+        kb_ids = search_config.get("kb_ids", [])
     e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
     if not e:
         return get_error_data_result(message="Knowledgebase not found!")
 
-    embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
-    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT)
+    chat_id = ""
+    similarity_threshold = 0.3,
+    vector_similarity_weight = 0.3,
+    top = 1024,
+    doc_ids = []
+    rerank_id = ""
+    rerank_mdl = None
+
+    if search_config:
+        if search_config.get("chat_id", ""):
+            chat_id = search_config.get("chat_id", "")
+        if search_config.get("similarity_threshold", 0.2):
+            similarity_threshold = search_config.get("similarity_threshold", 0.2)
+        if search_config.get("vector_similarity_weight", 0.3):
+            vector_similarity_weight = search_config.get("vector_similarity_weight", 0.3)
+        if search_config.get("top_k", 1024):
+            top = search_config.get("top_k", 1024)
+        if search_config.get("doc_ids", []):
+            doc_ids = search_config.get("doc_ids", [])
+        if search_config.get("rerank_id", ""):
+            rerank_id = search_config.get("rerank_id", "")
+
+    embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
+    chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=chat_id)
+    if rerank_id:
+        rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id)
     question = req["question"]
-    ranks = settings.retrievaler.retrieval(question, embd_mdl, kb.tenant_id, kb_ids, 1, 12, 0.3, 0.3, aggs=False, rank_feature=label_question(question, [kb]))
+    ranks = settings.retrievaler.retrieval(
+        question=question,
+        embd_mdl=embd_mdl,
+        tenant_ids=tenant_id,
+        kb_ids=kb_ids,
+        page=1,
+        page_size=12,
+        similarity_threshold=similarity_threshold,
+        vector_similarity_weight=vector_similarity_weight,
+        top=top,
+        doc_ids=doc_ids,
+        aggs=False,
+        rerank_mdl=rerank_mdl,
+        rank_feature=label_question(question, [kb]),
+    )
     mindmap = MindMapExtractor(chat_mdl)
     mind_map = trio.run(mindmap, [c["content_with_weight"] for c in ranks["chunks"]])
     mind_map = mind_map.output